# Evolutionary Prompt Design for LLM-Based Post-ASR Error Correction

arXiv: https://arxiv.org/abs/2407.16370 · HTML: https://mlsanity.com/paper/2407.16370/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition](https://mlsanity.com/paper/2310.06434/) (2023)
- [N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space](https://mlsanity.com/paper/2303.00456/) (2023)
- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [Large Language Models are Efficient Learners of Noise-Robust Speech Recognition](https://mlsanity.com/paper/2401.10446/) (2024)
- [WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models](https://mlsanity.com/paper/2203.15863/) (2022)
- [Multi-task Language Modeling for Improving Speech Recognition of Rare Words](https://mlsanity.com/paper/2011.11715/) (2020)
- [Revisiting ASR Error Correction with Specialized Models](https://mlsanity.com/paper/2405.15216/) (2024)

## Built on by (most cited first, 4 of 4)

- [MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula](https://mlsanity.com/paper/2412.15655/) (2024)
- [Listening, Imagining & Refining: A Heuristic Optimized ASR Correction Framework with LLMs](https://mlsanity.com/paper/2509.15095/) (2025)
- [ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features](https://mlsanity.com/paper/2511.21088/) (2025)
- [Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses](https://mlsanity.com/paper/2610.15056/) (2026)

## Abstract (arXiv)

Building upon the strength of modern large language models (LLMs), generative error correction (GEC) has emerged as a promising paradigm that can elevate the performance of modern automatic speech recognition (ASR) systems. One representative approach is to leverage in-context learning to prompt LLMs so that a better hypothesis can be generated by the LLMs based on a carefully-designed prompt and an N-best list of hypotheses produced by ASR systems. However, it is yet unknown whether the existing prompts are the most effective ones for the task of post-ASR error correction. In this context, this paper first explores alternative prompts to identify an initial set of effective prompts, and then proposes to employ an evolutionary prompt optimization algorithm to refine the initial prompts. Evaluations results on the CHiME-4 subset of the Task 1 of the SLT 2024 GenSEC challenge show the effectiveness and potential of the proposed algorithms.